Analysis Templates

Ready-to-Use Frameworks for Common Analyses

These templates provide step-by-step guides and AI prompts for common analysis tasks.

Template 1: Basic Data Exploration

When to Use

First look at any new dataset.

Steps

  1. Understand structure (rows, columns, types)
  2. Check data quality (missing, duplicates, errors)
  3. Generate summary statistics
  4. Create key visualizations
  5. Note questions and findings

AI Prompt

Help me explore this dataset.

Here's a sample of my data:
[Paste first 10-20 rows]

Please provide:
1. Description of each column
2. Data types and quality assessment
3. Summary statistics for numeric columns
4. Frequency counts for categorical columns
5. Suggested visualizations
6. Questions this data could answer
7. Potential issues to investigate

Template 2: Time Trend Analysis

When to Use

Understanding how a metric changes over time.

Steps

  1. Plot the metric over time
  2. Calculate period-over-period change
  3. Identify patterns (trend, seasonality)
  4. Find outliers or breaks
  5. Segment if relevant
  6. Project forward if needed

AI Prompt

Help me analyze this time series.

Data: [Metric by date/period]
Time range: [Start to end]
Context: [What this metric represents]

Please analyze:
1. Overall trend direction and magnitude
2. Seasonality or cyclical patterns
3. Notable outliers or change points
4. Period-over-period comparisons
5. What might explain observed patterns
6. Simple forecast if appropriate

Template 3: Comparison Analysis

When to Use

Comparing performance across groups, segments, or time periods.

Steps

  1. Define what you're comparing
  2. Establish metrics
  3. Calculate metrics for each group
  4. Visualize differences
  5. Assess significance of differences
  6. Investigate drivers

AI Prompt

Help me compare these groups.

Groups being compared: [Group A vs. B vs. C]
Metrics to compare: [What measurements]
Data: [Provide data or summary]

Please analyze:
1. Comparison of key metrics
2. Size of differences
3. Visualization of comparison
4. Whether differences are meaningful
5. Possible explanations
6. Recommended actions

Template 4: Cohort Analysis

When to Use

Understanding how groups defined by start date behave over time.

Steps

  1. Define cohorts (by signup date, first purchase, etc.)
  2. Track metric over time for each cohort
  3. Create cohort table
  4. Visualize cohort curves
  5. Compare early vs. recent cohorts
  6. Identify patterns

AI Prompt

Help me analyze these cohorts.

Cohort definition: [How groups are defined]
Metric tracked: [What you're measuring]
Time dimension: [Days/weeks/months since start]
Data: [Provide cohort data]

Please analyze:
1. How each cohort performs over time
2. Differences between cohorts
3. Trends across cohorts
4. What this suggests about behavior change
5. Implications for the business

Template 5: Funnel Analysis

When to Use

Understanding conversion through stages.

Steps

  1. Define funnel stages
  2. Count at each stage
  3. Calculate stage-to-stage conversion
  4. Calculate overall conversion
  5. Identify drop-off points
  6. Segment by relevant factors

AI Prompt

Help me analyze this funnel.

Stages: [List stages in order]
Counts at each stage: [Numbers]
Context: [What this funnel represents]

Please analyze:
1. Conversion rate at each step
2. Overall funnel conversion
3. Where the biggest drop-offs occur
4. Comparison to benchmarks if relevant
5. Recommendations for improvement

Template 6: Pareto (80/20) Analysis

When to Use

Understanding concentration — which items drive most of the results.

Steps

  1. Sum total metric (sales, issues, etc.)
  2. Calculate each item's contribution
  3. Sort descending by contribution
  4. Calculate cumulative percentage
  5. Find the 80% mark
  6. Identify top contributors

AI Prompt

Help me do a Pareto analysis.

Data: [Items and their values]
Context: [What these represent]

Please analyze:
1. Sort items by contribution
2. Calculate cumulative percentages
3. Identify items driving 80% of total
4. Visualize the distribution
5. Implications for focus

Template 7: Variance Analysis

When to Use

Understanding difference between actual and expected/budget.

Steps

  1. Calculate variance (actual - expected)
  2. Calculate variance percentage
  3. Categorize variances
  4. Identify largest variances
  5. Investigate causes
  6. Recommend responses

AI Prompt

Help me analyze variances.

Actual results: [Values]
Expected/budget: [Values]
Categories: [How to break down]

Please analyze:
1. Overall variance
2. Variance by category
3. Largest positive and negative variances
4. Possible explanations
5. Actions to address negative variances

Template 8: Correlation Check

When to Use

Testing whether two variables are related.

Steps

  1. Gather paired data
  2. Calculate correlation coefficient
  3. Create scatter plot
  4. Interpret strength and direction
  5. Consider causation carefully
  6. Note limitations

AI Prompt

Help me analyze the relationship between these variables.

Variable 1: [Describe]
Variable 2: [Describe]
Data: [Paired values]

Please analyze:
1. Calculate correlation
2. Interpret strength and direction
3. Create appropriate visualization
4. Discuss whether causation is likely
5. Note caveats and limitations

Using These Templates

Customize

Adapt templates to your specific context. Add or remove steps as needed.

Combine

Complex analyses often combine multiple templates.

Document

Keep records of analyses for future reference and reproducibility.

Pick a template. Answer a question. Build from there.

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